Comprehensive Summary
The research is primarily focused on developing a safe and efficient automatic method for preoperative planning of transcatheter mitral valve interventions. A retrospective study was conducted wherein data from 71 prior patients was collected, then fed to three deep learning models. These models were trained to segment the area surrounding the mitral valve annulus, while the annulus itself was extracted and clinical measurements were generated from the segments. These include 2-dimensional perimeter, trigone-to-trigone (TT) distance, septal-to-lateral (SL) distance, and commissure-to-commissure (IC) distance. These findings were then cross validated using k-folding.
Outcomes and Implications
Through the automated preoperative planning method generated by the study, planned mitral valve interventions can be both safer and more efficient. The model reduces the analysis time per each patient from 25 minutes (on average) to less than a second, providing much more time to healthcare workers for other aspects of patient care. The measurements created by the model are also very accurate (R2 values ranging from 0.86 to 0.93), which is extremely necessary in these high-risk cases, and solidifies this method as a concrete option for clinical practices with live patients.